DNA methylation of Vesicular Glutamate Transporters in the mesocorticolimbic brain following early life stress and adult ethanol exposure-an ...
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OPEN DNA methylation of Vesicular
Glutamate Transporters
in the mesocorticolimbic brain
following early‑life stress and adult
ethanol exposure—an explorative
study
Maria Vrettou1, Liying Yan2, Kent W. Nilsson3,4, Åsa Wallén‑Mackenzie5, Ingrid Nylander6 &
Erika Comasco1*
DNA methylation and gene expression can be altered by early life stress (ELS) and/or ethanol
consumption. The present study aimed to investigate whether DNA methylation of the Vesicular
Glutamate Transporters (Vglut)1-3 is related to previously observed Vglut1-3 transcriptional
differences in the ventral tegmental area (VTA), nucleus accumbens (Acb), dorsal striatum (dStr) and
medial prefrontal cortex (mPFC) of adult rats exposed to ELS, modelled by maternal separation, and
voluntary ethanol consumption. Targeted next-generation bisulfite sequencing was performed to
identify the methylation levels on 61 5′-cytosine-phosphate-guanosine-3′ sites (CpGs) in potential
regulatory regions of Vglut1, 53 for Vglut2, and 51 for Vglut3. In the VTA, ELS in ethanol-drinking rats
was associated with Vglut1-2 CpG-specific hypomethylation, whereas bidirectional Vglut2 methylation
differences at single CpGs were associated with ELS alone. Exposure to both ELS and ethanol, in the
Acb, was associated with lower promoter and higher intronic Vglut3 methylation; and in the dStr,
with higher and lower methylation in 26% and 43% of the analyzed Vglut1 CpGs, respectively. In
the mPFC, lower Vglut2 methylation was observed upon exposure to ELS or ethanol. The present
findings suggest Vglut1-3 CpG-specific methylation signatures of ELS and ethanol drinking, underlying
previously reported Vglut1-3 transcriptional differences in the mesocorticolimbic brain.
Adversity during early life has been linked to psychopathology later in life, including alcohol use disorder
(AUD)1–3. Notably, early life stress (ELS) can lead to epigenetic modifications, such as alterations in DNA meth-
ylation patterns, which can in turn affect gene expression4. DNA methylation, catalyzed by DNA (cytosine-
5)-methyltransferases (DNMTs), is a chemical modification, suggested to serve as a signature of early life experi-
ences that can imprint on the developing brain, explaining why those early experiences have a long-lasting e ffect1.
In rats, ELS can be modeled via maternal separation (MS) daily for 60 to 360 min, whereas MS for only
15 min (MS15) is more naturalistic, thus serving as control c ondition5. MS360 has been associated with higher
alcohol consumption5, while MS15 leads to hypo-reactive hypothalamic–pituitary–adrenal (HPA) axis to stress6
and has a protective role towards alcohol intake5. The effect of MS is mediated via disturbances in mother–pup
interactions and maternal care towards the offspring5,6. Differences in maternal care in rats and humans have
been associated with altered HPA-axis response to stress and negative mental health via differential methylation
of the promoter of the glucocorticoid receptors7,8.
Not only ELS, but also ethanol consumption per se, can affect DNA methylation patterns9. Chronic ethanol
exposure has been associated with global DNA hyper-methylation in various brain regions of r odents9, and
1
Department of Neuroscience, Science for Life Laboratory, Uppsala University, Uppsala, Sweden. 2EpigenDx,
Inc, Hopkinton, MA, USA. 3Centre for Clinical Research Västerås, Uppsala University, Västmanland County
Hospital Västerås, Uppsala, Sweden. 4The School of Health, Care and Social Welfare, Mälardalen University,
Västerås, Sweden. 5Department of Organismal Biology, Uppsala University, Uppsala, Sweden. 6Department of
Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. *email: Erika.comasco@neuro.uu.se
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specifically associated with increased DNMT1 expression in the nucleus accumbens (Acb) of adult m ice10 and
in the Acb and medial prefronal cortex (mPFC) of post-dependent rats after weeks of abstinence to e thanol11.
On the contrary, though measured in blood, a recent epigenome-wide association study (EWAS) in humans
showed greater alcohol intake to be associated with lower global DNA m ethylation12. Yet, the effects of both
ELS and ethanol on DNA methylation in the young adult non-dependent brain are largely unknown. Indeed, to
disentangle the biological underpinnings of the initial effects of ethanol use can contribute to shed light on the
potential neural mechanisms leading to the development of a ddiction9.
Altered glutamatergic neurotransmission has been implicated in various phases of the addiction cycle, from
initial and voluntary to chronic and compulsory alcohol u se13,14. This is not of surprise considering that glutamate
is the primary excitatory neurotransmitter in the brain15. Via its interaction with the mesocorticolimbic dopamin-
ergic system, glutamatergic neurotransmission mediates alcohol-related reward, whereas a hyperglutamatergic
state is central to the development of alcohol dependence16,17. Key regions of the mesocorticolimbic system are
the ventral tegmental area (VTA), which projects to the Acb and the mPFC. Altered glutamatergic transmission
is also seen upon both acute and chronic stress in the PFC and midbrain dopamine neurons18. Furthermore, ELS
has been shown to result in increased excitatory glutamatergic neurotransmission in the paraventricular nucleus
of the h ypothalamus19, and in disturbed homeostasis of glutamatergic s ynapses20, as well as in aberrant reward
processing and drug-seeking behaviors, likely mediated by disturbances in dopaminergic and glutamatergic
neurotransmission in the PFC and the Acb21.
To date, the best markers for the glutamatergic phenotype are the Vesicular Glutamate Transporters (VGLUTs)
1–3 (encoded by the solute carrier superfamily genes Slc17a7/Vglut1, Slc17a6/Vglut2 and Slc17a8/Vglut3, respec-
tively), as every neuron that expresses a Vglut gene has the ability to release glutamate22,23. That is because
VGLUTs actively package glutamate into presynaptic vesicles in neurons through an electrochemical proton-
dependent gradient24. Lately, a number of studies has shown a role of VGLUTs in reward and addiction, includ-
ing alcohol-related phenotypes in both humans25–28 and rodents29–36. Altered DNA methylation patterns of the
Vglut2 promoter in the murine hippocampus have been observed as a result of prenatal ethanol e xposure37. A
recent EWAS study found an association between alcohol intake and two CpGs within the body of VGLUT1 and
the promoter of VGLUT2 in h umans12. Yet, the interplay between both ELS and adult ethanol consumption on
Vgluts DNA methylation patterns in the brain of young adults has never been studied.
In fact, the interplay between ELS and ethanol was only recently investigated on Vgluts gene expression in
young adult outbred Wistar r ats30. More specifically, previous analysis provided evidence of differential Vglut1-
3 expression in the mesocorticolimbic system [including the VTA, Acb, dorsal striatum (dStr)], and mPFC
of adult outbred Wistar rats who had been previously exposed, during the first three postnatal weeks, to ELS
as compared to control, as well as to voluntary consumption of ethanol or water during adulthood30. Altered
expression of Dnmt1 and Mecp2 (methyl-CpG-binding protein 2), two key regulatory genes of DNA methylation
and the transcription machinery, was also observed as a result of the interaction between ELS and ethanol. The
effect was following the same direction as on Vglut3 in the Acb, but opposite direction than Vglut1 in the dStr,
thus suggesting a potential involvement of the epigenetic machinery in the observed differences of striatal Vgluts
expression30. Alterations in DNA methylation of potential regulatory regions within the promoter or gene body
of Vglut1-3 could be a plausible epigenetic mechanism behind the abovementioned differences in gene expres-
sion, and therefore were here studied. The present study sought to investigate the effect of ELS and adult ethanol
consumption on Vglut1-3 DNA methylation levels in the mesocorticolimbic young adult rat brain of relevance
to the transcriptional differences previously observed in the same animals. Furthermore, correlations between
DNA methylation, and expression, blood corticosterone levels as well as ethanol intake were tested to assess
whether they reflect any observed stress or ethanol effects. Finally, moderation effects of DNA methylation on
mRNA expression levels were explored.
Materials and methods
Animal experiment. The present study is based on the animal experiment presented in30 and illustrated
in Figure S1. Briefly, adult male Wistar rats (n = 50) were exposed to MS (MS15: control or MS360: ELS) during
the first 3 postnatal weeks (PNW), and to water-only [MS15W (n = 10); MS360W (n = 10)] or voluntary alcohol
(20%) exposure [MS15E (n = 10); MS360E (n = 20)] during the dark cycle in adulthood (PNW 10–16). Outbred
Wistar rats were used to mirror the individual differences and heterogeneity of alcohol-drinking patterns38. Only
male rats were used to minimize the potential confounding effect of hormonal fluctuations during the estrous
cycle in f emales39, as well as based on previous data that alcohol intake in adult female rats was not affected by
the MS paradigm40. At the end of each session, the ethanol and water intake was quantified by weighing the
bottles. The rats were sacrificed at PNW16, immediately after the end of the last two-hour drinking session, and
the brain as well as trunk blood for corticosterone analysis were collected and stored at − 80 °C. The study was
approved by the Uppsala Animal Ethical Committee (C32/11) and followed the guidelines of the Swedish Legis-
lation on Animal Experimentation (Animal Welfare Act SFS1998:56) and the European Communities Council
Directive (86/609/EEC). The study was carried out in compliance with the ARRIVE guidelines.
Blood corticosterone levels (ng ml−1) were measured using the ImmuChem Double Antibody Corticosterone
125I RIA kit for rats and mice (MP Biomedicals, Orangeburg, NY, USA), as described in41 and are reported in
Table S8. DNA/RNA was isolated from the rat VTA, Acb, mPFC and dStr, using AllPrep DNA/RNA/miRNA
Universal Kit according to the manufacturer’s protocol (Qiagen AB Sollentuna, Sweden), and quantified using
a Nanodrop ND 1000 spectrometer. RNA was converted to cDNA and used to assess the expression of Slc17a7/
Vglut1, Slc17a6/Vglut2, Slc17a8/Vglut3, Dnmt1 and Mecp2 relative to three housekeeping genes (Actb, Gapdh
and Rpl19) by real time PCR, as described i n30. For the present study, the DNA was used for DNA methylation
analyses. To minimize potential noise and cost of downstream DNA methylation analyses, for each gene and
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Figure 1. Schematic representation of the DNA methylation analyses using targeted next-generation bisulfite
sequencing (tNGBS). Acb nucleus accumbens, dStr dorsal striatum, VTA ventral tegmental area, mPFC medial
prefrontal cortex.
brain region, samples were selected on the basis of within-group homogeneity in terms of direction of gene
expression differences compared to the respective control group [e.g. if MS360E group had significantly higher
expression compared to MS15E group, rats within MS360E were assessed individually (by calculating the log-
2fold difference relative to the mean MS15E expression) to check whether their expression levels were higher
(same direction) or lower (opposite direction) than the mean MS15E mRNA levels]. Thus, high within-group
heterogeneity led to individual-sample DNA methylation analysis for Vglut1 and Vglut2 in the VTA (n = 10/group
for MS15W, MS360W, MS15E; n = 18 in MS360E), but pooled-sample DNA methylation analyses for Vglut1 in
the dStr (n = 10/group for MS15W, MS360W, MS15E; n = 17 in MS360E), Vglut3 in the Acb (n = 10/group for
MS15W, MS360W, MS15E; n = 17 in MS360E) and Vglut2 in the mPFC [n = 10 (MS15W), n = 8 (MS360W), n = 9
(MS15E) and n = 20 (MS360E)], for which homogeneous within-group expression patterns were observed. After
the exclusion of the aforementioned samples, the transcriptional levels of Vglut1-3 were re-tested confirming
that the previously reported differences30 remained unchanged (data not shown).
Targeted next generation bisulfite sequencing. Targeted next generation bisulfite sequencing
(tNGBS) was performed to identify differentially methylated CpG sites in target CpG regions on 48 samples for
Vglut1 and Vglut2 in the VTA, and 4 pooled samples (one for each experimental group) for Vglut1 in the dStr,
Vglut3 in the Acb and Vglut2 in the mPFC. All methylation analyses were performed by EpigenDx Inc (Hopkin-
ton, MA, USA). A flowchart of the procedure is shown in Fig. 1.
Vglut1-3 gene sequences (+/− 5000 bp including the target array) were acquired from Ensembl genome
browser, annotated and converted to a bisulfite sequence using EpigenDx’s Bisulfite Sequence Converter. The
target sequences were re-evaluated against UCSC genome browser for repeat sequences and those containing
repetitive elements, low sequence complexity, high thymidine content and overall CpG density were excluded
for in silico design process. Regions with differential methylation in human gene were retrieved from ENCODE/
HAIB and used to design the correspondent rat methylation assays (Figure S2). The 41 designed assays [14 for
Vglut1 (VTA, dStr), 13 for Vglut2 (VTA, mPFC) and 14 for Vglut3 (Acb), Table S1] were first grouped by gene,
and then by GC %, amplicon size, and design score. A gradient PCR was run on each group of assays at different
annealing temperatures using stock bisulfite-modified DNA; the most successful annealing temperature was
chosen for sequencing. Assays failing PCR optimization were excluded from analyses.
DNA samples (300 ng) were bisulfite modified using Zymo EZ- 96 DNA Methylation Kit (Zymoresearch,
CA, USA) as per manufacturer’s protocol with minor modification and were eluted using M-elution buffer in
46 µl. EpigenDx’s custom library preparation method was performed on the chosen test samples, which were
then templated using the Ion Chef System (Thermo Fisher, CA, USA) and sequenced using the Ion S5 Sequencer
(Thermo Fisher, CA, USA). Read counts from this sequencing run was used to regroup all the assays into final
multiplex PCR conditions (Table S1). PCRs included 0.5 units of Qiagen HotStarTaq (Qiagen, MD, Catalogue
number 203205), 0.2 µM primers, and 2 µl of bisulfite-treated DNA in a 20 µl reaction. All PCR products were
verified and quantified using the QIAxcel Advanced System (Qiagen, Germany). Prior to library preparation,
PCR products from the same sample were pooled and purified using QIAquick PCR Purification Kit columns
(Qiagen, MD, USA). The final number of successful assays (> 30 reads) was 33 (11 for Vglut1, 10 for Vglut2 and
12 for Vglut3). The regions of interest and targeted CpGs for each gene are shown in Fig. 2a–c.
Next, libraries were prepared for all test samples, library molecules were purified using Agencourt AMPure
XP beads (Beckman Coulter Inc., CA, USA) and quantified using the QIAxcel Advanced System (Qiagen, Ger-
many). Barcoded samples were then pooled in an equimolar fashion before template preparation and enrichment
were performed on the Ion Chef System (Thermo Fisher Scientific Inc., MA, USA) using Ion 520 & Ion 530 ExT
Chef reagents. Following this, enriched, template-positive library molecules were then sequenced on the Ion S5
Sequencer using an Ion 530 sequencing chip (Thermo Fisher Scientific Inc., MA, USA).
Data analysis. FASTQ files from the Ion Torrent S5 server were aligned to the local reference database using
open-source Bismark Bisulfite Read Mapper with the Bowtie2 alignment algorithm (https://www.bioinforma
tics.babraham.ac.uk/projects/bismark/)42. Methylation levels were calculated in Bismark by dividing the number
of methylated reads by the total number of reads.
Statistical analysis. The methylation levels of single CpG sites were tested for normality using the Shapiro–
Wilk test. The grand majority was not normally distributed, but showed homogeneity of variances (p > 0.05), as
tested with the modified Levene’s test, based on medians. Thus, interactive effects between ELS and ethanol were
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Figure 2. CpGs analyzed within target CpG regions for DNA methylation analysis across the promoter region
and gene body of Slc17a7/Vglut1 (a); Slc17a6/Vglut2 (b); Slc17a8/Vglut3 (c). The name of each designed assay is
written above the black arrows. In red boxes, the genomic location (in base pairs from ATG) is depicted.
ormality43, and between-group differences
assessed using a two-way ANOVA, which is robust to violations of n
were investigated with the non-parametric Mann–Whitney U test. Correlations between methylation levels and
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Gene Variable MS15W MS360W MS15E MS360E
CpG -56 (5’ upstream)* CpG -104 (5’upstream)* CpG 126 (exon 7)*
CpG -59 (5’ upstream)* CpG 207 (exon 12)* CpG 147 (exon 9)**
CpG 124 (exon 7)* CpG 207 (exon 12)*
CpG 149 (exon 9)*
CpG -103 (5’ upstream)** CpG -57 (5’ upstream)* CpG -59 (5’ upstream)* CpG 163 (intron 9)*
Gene expression
CpG 199 (exon 12)** CpG 145 (intron 8)* CpG 163 (intron 9)*
CpG 209 (3’ UTR)* CpG 150 (exon 9)*** CpG 195 (exon 12)*
CpG 152 (exon 9)**
Vglut1
CpG 164 (intron 9)*
CpG 165 (intron 9)***
CpG 196 (exon 12)**
CpG 201 (exon 12)*
CpG 155 (exon 9)* CpG 147 (exon 9)*** CpG -122 (5’ upstream)*
Cor costerone
CpG 208 (exon 12)*
CpG -104 (5’ upstream)** CpG 194 (exon 12)* CpG 197 (exon 12)** CpG -56 (5’ upstream)*
CpG 201 (exon 12)*
CpG -41 (5’ upstream)** CpG 58 (intron 2)* CpG -39 (5’ upstream)*
Gene expression
CpG 18 (intron 1)* CpG -35 (5’ upstream)*
CpG 77 (intron 2)** CpG 53 (intron 2)* CpG -18 (5’ UTR)*
CpG 56 (intron 2)*** CpG -2 (5’ UTR)**
CpG 60 (intron 2)* CpG 1 (exon 1)*
CpG 110 (intron 3)** CpG -18 (5’ UTR)*** CpG -36 (5’ UTR)* CpG 58 (intron 2)*
Vglut2
CpG 111 (intron 3)* CpG -33 (5’ UTR)*
Cor costerone
CpG -42 (5’ upstream)* CpG -37 (5’ UTR)** CpG 1 (exon 1)* CpG -20 (5’ UTR)*
CpG 112 (intron 3)* CpG 16 (intron 1)** CpG -18 (5’ UTR)**
CpG 109 (intron 3)* CpG 24 (intron 1)*
CpG 111 (intron 3)* CpG 77 (intron 2)*
CpG 111 (intron 3)*
Table 1. Correlations between CpG methylation and gene expression as well as blood corticosterone levels
for Slc17a7/Vglut1 and Slc17a6/Vglut2 genes in the VTA by group. The color bar indicates the Spearman´s rho
coefficient; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001.
gene expression as well as with corticosterone levels (Table 1) and ethanol intake during the last drinking week
(PNW15) before decapitation, and during the last drinking week (PNW16) (Table S10b) were assessed by the
non-parametric Spearman co-efficient. PNW15 was chosen for representing better the individual drinking pat-
tern without any extra potential stress effect due to people entering the room to terminate the experiment30.
Based on PNW15, three subgroups were defined with distinct ethanol intake levels (i.e. high: > 1.5 g/kg/2 h;
moderate: 1–1.5 g/kg/2 h and low: < 1 g/kg/2 h drinkers)30; Table S10a. Among those subgroups, correlations
between methylation and gene expression as well as ethanol intake during PNW15 and PNW16, which is likely
more affected by the last ethanol consumption before decapitation, were also assessed (Table S10c).
Moderated moderation analysis was performed to assess the three-way interaction between rearing [MS15
vs. MS360 (ELS)] and drinking (water vs. ethanol) and Vglut2 methylation on Vglut2 expression, using three-
way ANOVA and SPSS PROCESS macro v2.1644. The Johnson-Neyman technique was used to determine the
region of significance in the distribution of methylation levels where the interaction between ELS and ethanol
on Vgluts expression was s ignificant44.
Differences in methylation of each CpG site are reported as absolute percentages (%), and relative (%) dif-
ferences within brackets. For methylation data of the pooled samples (Vglut1 in the dStr, Vglut3 in the Acb and
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Figure 3. Mean Vglut1 and Vglut2 methylation (%) of CpGs associated with interactive effects of ELS and
ethanol (ELS x E), tested with two-way ANOVA, and differences between the groups previously found to vary
in Vglut1-2 mRNA levels (i.e. Vglut1: ^MS360E, n = 18 vs. M15E, n = 10; Vglut2: #MS360W vs. MS15W, n = 10/
group) in the ventral tegmental area (VTA), tested with Mann–Whitney U test. In boxes, the transcription
factors predicted to bind to the respective site in rat are shown. CEBPA/D CCAAT/enhancer binding protein-
alpha/delta, ELS early life stress, E ethanol, MS maternal separation, USF1 Upstream Transcription Factor 1,
UTRuntranslated region, W water; Error bars: 95% confidence intervals; * MS360E vs. MS360W; # for MS360W
vs. MS15W; ¤ MS15E vs. MS15W; ^ MS360E vs. MS15E; p ≤ 0.05. Vglut1: Between-group comparisons showed
that MS360E rats had lower methylation than MS15E group within exon 9 (CpG149: U = 44; p = 0.027) and
12 (CpG197: U = 36; p = 0.009; CpG198: U = 33; p = 0.005). In silico analysis identified potential binding sites
for CCAAT/enhancer binding proteins (C/EBP)-delta at CpG198. Vglut2: Interactive effects of ELS and
ethanol drinking were seen on methylation of 6 CpGs within the 5’-upstream, and intron 1–3 of Vglut2. ELS
in ethanol-drinking rats resulted in 0.4–3.7% (19–50%) lower methylation within 5’-upstream (CpG-40),
intron 1 (CpG17), 2 (CpG60) and 3 (CpG107, 115, 116), but in 0.25–1.3% (23–55%) higher methylation in
no-ELS or water-drinking counterparts (GpG -40: F (1, 44) = 6.526; p = 0.014, partial eta-square = 0.129; adj.
R-square = 0.089); CpG17: F (1, 44) = 5.474; p = 0.024, partial eta-square = 0.111; adj. R-square = 0.136; CpG60:
F (1, 44) = 6.467; p = 0.015, partial eta-square = 0.128; adj. R-square = 0.076; CpG107: F (1, 44) = 5.175; p = 0.028,
partial eta-square = 0.105; adj. R-square = 0.072; CpG115: F (1, 44) = 4.149; p = 0.048, partial eta-square = 0.086;
adj. R-square = 0.047; CpG116: F (1, 44) = 10.848; p = 0.002, partial eta-square = 0.197; adj. R-square = 0.251)..
Between-group comparisons showed that these effects were driven by lower methylation of CpG-40 (U = 44.5;
p = 0.027) upon ethanol in ELS-rats (MS360E vs. MS360W) and of CpG17 (U = 32; p = 0.004), 107 (U = 48.5;
p = 0.045), and 116 (U = 28; p = 0.002), upon ELS in ethanol-drinking rats (MS360E vs. MS15E), but higher
methylation of CpG17 (U = 21; p = 0.029), 115 (U = 24; p = 0.05), and 116 (U = 11; p = 0.002), upon ethanol-
only (MS15E vs. MS15W). Between-group comparisons showed that MS360W rats had higher methylation
within the 5’-upstream (CpG-36: U = 18; p = 0.015), intron 2 (CpG59: U = 18; p = 0.015) and 3 (CpG112: U = 21;
p = 0.029), but lower methylation within exon 1 (CpG2: U = 22; p = 0.035) and intron 3 (CpG110: U = 20;
p = 0.023) compared to controls (MS15W). In silico analysis identified potential binding sites of Upstream
Transcription Factor (USF)-1 at CpG59 (intron 2).
Vglut2 in the mPFC), for which no statistics could be performed (Table S5-7), CpGs with > 5% difference in
absolute values of methylation levels between the groups are reported. Statistical analyses were performed using
SPPS (IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp).
Transcriptional factor binding sites (TFBS) analysis. Potential TFBS in rat (Rattus Norvegicus)
Vglut1-3 were assessed using ALGGEN PROMO45,46 which predicts TFBSs using TRANSFAC database version
8 (http://alggen.lsi.upc.es/cgi-in/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3) and are depicted in Figs. 3,
4, 5, 6.
Results
CpGs in potentially regulatory regions of Vglut1-3 were analyzed using tNGBS. Gene, but not brain region-,
specific CpG methylation patterns were observed. CpG-specific methylation differences were noticed between
the groups previously found to differ in their Vglut expression levels30. High correlations (r > ± 0.7) between
CpG-methylation and gene expression or corticosterone levels were observed in all groups, but they were sparse
and moderate (rwww.nature.com/scientificreports/
Figure 4. DNA methylation of Vglut1 CpG sites that differed more than 5% among groups previously found
to differ in Vglut1 expression levels in the dorsal striatum (dStr) (i.e. * MS360E, n = 17 vs. MS360W, n = 10; ^
MS360E, n = 17 vs. MS15E, n = 10)30 (a), and within the 5’-upstream in the dStr (b). In boxes, the transcription
factors predicted to bind to the respective site in rat are shown. AP1 Activator protein 1, CEBPA CCAAT/
enhancer binding protein-alpha, CREB cAMP Responsive Element-Binding Protein. E ethanol, MS maternal
separation, NF1 Nuclear factor 1, SRF Serum response factor, USF1-2 Upstream Transcription Factor 1–2, UTR
untranslated region; W water; ¤ > 2 – threefold difference in % methylation when compared to MS360E.
CpG methylation patterns in Vglut1 targeted regions and its associations with ELS and eth‑
anol consumption. Methylation data was available for 61 CpG sites for Vglut1 (Table S2). In the dStr,
methylation analysis failed in MS360E group for 15 CpGs (25%). In the control group (MS15W), CpGs in the
promoter region of Vglut1 were virtually unmethylated (median: 0- 8%) in both the VTA and dStr, except for 3
CpGs with high methylation (65–86%). CpGs in intronic regions were highly methylated (65–88%) as well as
in exonic regions (62–95%) with few exceptions with moderate methylation (23–56%) (Table S5). In the VTA
(Fig. 3), ELS in ethanol-drinking rats (MS360E vs MS15E) was associated with hypo-methylation of Vglut1 exon
9 and 12 (3 CpGs). The combination of ELS and ethanol (in MS360E) showed bidirectional effects on Vglut1
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Figure 5. DNA methylation of Vglut3 CpG sites that differed more than 5% among groups previously found to
differ in Vglut3 expression levels in the nucleus accumbens (Acb) (i.e. *MS360E, n = 17 vs. MS360W, n = 10 and
^MS360E, n = 17 vs. MS15E, n = 10)30. In boxes, the transcription factors predicted to bind to the respective site
in rat are shown. CEBPD CCAAT/enhancer binding protein-delta, CREB cAMP Responsive Element-Binding
Protein, E ethanol, MS maternal separation, USF1 Upstream Transcription Factor 1, UTRuntranslated region,
W water.
methylation in the dStr (Fig. 4), leading to hypermethylation (> 5%) in a total of 20% of the analyzed CpGs, the
majority within intron 9, while 26%, most of them in exon 12, were hypomethylated, compared to both MS360W
and MS15E. When considering smaller differences (1–5%) in the 5’-upstream, MS360E had a total of 26% of
all analyzed CpGs hypermethylated, while 43% were hypomethylated, compared to both MS360W and MS15E.
CpG methylation patterns in Vglut2 targeted regions and its associations with ELS and eth‑
anol consumption. Methylation data was available for 53 CpG sites for Vglut2 (Table S3). CpGs in the
promoter region, 5’-UTR and exon 1 of Vglut2 were virtually unmethylated (0–11%), while the range in meth-
ylation was wider in intronic regions (0–46%) in both the VTA and mPFC (Table S7). In the VTA, ELS in water-
drinking rats had a bidirectional effect on Vglut2 CpG methylation (5 CpGs), while interaction between ELS
and ethanol (driven by differences in MS360E vs. MS15E or MS360W) was associated with hypo-methylation
of Vglut2 promoter and gene body (6 CpGs). Vglut2 CpG methylation did not moderate the previously found
interactive effect of ELS and ethanol on Vglut2 expression30. Lastly, ELS- or ethanol-only were largely associated
with lower Vglut2 CpG-specific methylation in the mPFC (Fig. 6).
CpG methylation patterns in Vglut3 targeted regions and its associations with ELS and ethanol
consumption. Methylation data was available for 51 CpG sites for Vglut3 (Table S4). Contrary to Vglut1-2
methylation pattern, Vglut3 CpGs (Acb) in the promoter region were highly methylated (75–97%) as well as
in exon 12 (83–98%), but more moderately in 5’-UTR (31–71%), while the range in methylation was wider in
intronic regions (39–97%) with an exception of CpG316 that had low methylation (8%) (Table S6). ELS and
ethanol were associated with lower promoter, but higher intronic Vglut3 CpG methylation in the Acb (Fig. 5).
Discussion
The present explorative study shows nominally-significant associations between ELS, voluntary ethanol con-
sumption, and CpG-specific Vglut1-3 methylation in the VTA, Acb, dStr and mPFC of outbred adult male rats,
as well as correlations between Vglut1-2 methylation and expression, blood corticosterone levels and ethanol
consumption in the VTA. Vgluts methylation varied in a gene- or brain region-dependent way, in the groups
previously found to differ in their Vglut mRNA expression levels30, whereas there was not a pattern of associa-
tion between these effects and the gene targeted regulatory region (i.e. promoter or gene body). These DNA
methylation differences could possibly serve as a mechanism behind the previously reported differences in Vgluts
expression30, via potential changes in binding of distinct transcriptional factors. Even though small-in-magnitude
differences are ambiguous in regards to their biological significance47, subtle differences (1–10%) in methylation
of single CpG sites, such the ones found here, have a potential of functional relevance, not likely as on/off switch
of gene expression, but rather in redistribution of the transcriptional landscape, affecting translational isoform
production and the proteome48. Correlations between Vgluts expression and methylation were present in all
groups, but were sparse in the ethanol-drinking ELS-rats, suggesting that the molecular mechanisms regulating
gene expression and DNA methylation are potentially different in the presence of two aversive environmental
factors (ELS and ethanol) as compared to only one or none. Moreover, in the VTA, CpG-specific methylation
of both Vglut1 and 2 was correlated with ethanol intake levels in MS15E and MS360E, but also within the three
MS360E subgroups during the last two drinking weeks. Few distinct correlations emerged or others were lost
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Figure 6. DNA methylation of Vglut2 CpG sites in the mPFC that differed more than 5% among groups
previously found to differ in Vglut2 expression levels in the medial prefrontal cortex (mPFC) (i.e. MS360W, n = 8
vs. MS15W, n = 10 and MS15E, n = 9 vs. MS15W) 30 within intron 1–3 (a), and within 5’-upstream and 5’-UTR
(b). In boxes, the transcription factors predicted to bind to the respective in rat site are shown. AP1 Activator
protein 1, E ethanol, HNF3B Hepatocyte nuclear factor-3 beta, MS maternal separation, USF1 Upstream
Transcription Factor 1, UTRuntranslated region, W water; #: 0.5—onefold and ¤: > 1—threefold difference in %
methylation compared MS360W or MS15E to MS15W.
when assessing PNW16 compared to PNW15, but the vast majority of the correlations (or their direction)
remained virtually the same between the two weeks in MS15E and MS360E, thus suggesting that the differences
in methylation were not due to the acute effect of ethanol.
Vglut1: In line with previous findings of higher Vglut1 expression in the VTA of MS360E rats compared
to MS15E30, we observed hypomethylation of CpG149 (exon 9) and of CpG197 and 198 (exon 12). A plausi-
ble mechanism could involve ELS- and ethanol-induced changes in transcriptional factor (TF) binding. TFs
control the levels of gene expression8, while methylation in non-CpG islands, and in vicinity (± 100 bp) to the
TFBS, could block the TFs b inding49. Herein, the TF C/EBP-delta was predicted to bind to CpG198. Chronic
ethanol consumption has been related with increased C/EBP-delta in the brain50, and herein high ethanol intake
(in MS360E) during the last week before decapitation was negatively correlated with CpG198 methylation
(Table S10). Lower methylation of CpG198, likely due to ethanol drinking in MS360E rats, could potentially
lead to increased C/EBP-delta binding contributing to higher Vglut1 expression. Drug-related epigenetic changes
in Vglut1 have been demonstrated; with cocaine treatment leading to downregulation of Vglut1 expression as
well as hypermethylation of the promoter region of the gene in the Acb of mice51, while in a recent EWAS study,
one CpG within the gene body of VGLUT1 was associated with alcohol intake in h umans12.
In the dStr, higher Vglut1 expression was also shown in the MS360E compared to both MS15E and MS360W
rats30. For the latter (MS360E vs. MS360W), lower Dnmt1 expression was also observed30. DNMT1 binds to
hemi-methylated sites and maintains DNA methylation signatures during DNA r eplication52. Thus, lower Dnmt1
expression was hypothesized to contribute to lower methylation of specific Vglut1 CpGs in the MS360E. Indeed,
MS360E had lower methylation of 30% of all analyzed CpGs mainly within 5’-upstream, intron 9 and exon 12,
compared to MS360W. Compared to both MS360W and MS15E, MS360E rats had a total of 43% of all analyzed
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CpGs, most of them in exon 12 and 5’-upstream, hypomethylated, while 26%, the majority in intron 9, were
hypermethylated. Within the 5’-upstream, smaller differences (1–5%) were in fact observed for the majority of
the analyzed CpGs. In the same region, and comparing the same groups (MS360E vs. MS15E and MS360W),
small-in-magnitude hypermethylation of CpG − 42 was previously observed in the promoter region of Monoam-
ine Oxidase A (Maoa)53, along with lower Maoa expression41. MAOA metabolizes monoamine neurotransmit-
ters such as dopamine and serotonine and has been constantly implicated in behavioral regulation, stress- and
alcohol-related phenotypes54.
Vglut2: Previous analysis showed an interactive ELS x ethanol effect on Vglut2 expression30; driven by ELS rats
(MS360W) displaying lower Vglut2 expression in the VTA, while ethanol had a specular effect leading to higher
Vglut2 expression in ELS rats (MS360E), but lower in no-ELS counterparts (MS15E). Moderation analysis did
not provide evidence that CpG methylation moderates the previously observed effect of ELS x ethanol on Vglut2
expression in the V TA30. However, and in line with this interactive effect, 6 CpGs within Vglut2 promoter and
gene body were hypomethylated in MS360E compared to MS15E or MS360W. Our findings corroborate well
with the study of Zhang et. al that showed adult Vglut2 up-regulation due to prenatal ethanol exposure, further
correlated with decreased DNA methylation of the promoter region in the murine hippocampus of m ales37.
Furthermore, the lower Vglut2 expression in MS360W compared to MS15W rats was accompanied by hyper-
methylation of CpG-36 (5’-upstream), 59 (intron 2), and 112 (intron 3), but hypomethylation of CpG2 (exon 1),
and 110 (intron 3). Lower Dnmt1 expression in the same animals30, may have contributed to the hypomethyla-
tion of CpG2, and 110, although no correlations between Dnmt1 expression and methylation of these sites were
observed (Table S9). DNMT3a and 3b, with a role in de novo methylation (Moore et al., 2013), might have also
played a role in the present methylation differences, and should be investigated.
Furthermore, lower Vglut2 expression was found in the mPFC in ELS-only (MS360W) or ethanol-only
(MS15E) compared to controls (MS15W)30. ELS was associated with hypomethylation of CpG75 (intron 2) and
CpG115-117 (intron 3), but hypermethylation of CpG59 (intron 2), similar to the ELS effect on the same CpG
in the VTA. The TF USF-1 is predicted to bind to CpG59; higher methylation of this site could contribute to
reduced binding of USF-1 and in turn to lower Vglut2 expression observed in both the mPFC and VTA of the
same group (MS360W vs. MS15W). In the mPFC, ethanol resulted largely in lower CpG-specific methylation
and especially within intron 3. This hypomethylation could potentially have contributed to the lower Vglut2
expression previously reported30, in line with the notion that methylation in gene body is associated with higher
gene expression49. Ethanol also resulted in lower CpG-specific methylation in the promoter of the stress-related
genes Pomc (proopiomelanocortin), Avp (arginine vasopressin) and Fkbp5 (FK506 Binding Protein 5) in the
pituitary and hypothalamus of the same r ats55.
Vglut3: In the Acb, lower Vglut3, and Dnmt1 expression was previously found in ELS ethanol-exposed rats,
whereas the opposite pattern was seen in the water-drinking counterparts30. The lower Dnmt1 was hypothesized
to contribute to lower methylation of specific Vglut3 CpGs in the MS360E group. Indeed, in the Acb of MS360E
rats, Vglut3 methylation was lower in the majority of targeted CpGs within the 5’-upstream, but higher in most
of analyzed CpGs within 5’-UTR, intron 1 and 2. Interestingly, CpG316 in intron 2 had more than twofold higher
methylation in MS360E compared to MS15E. The TFs USF-2 and CREB were predicted to bind at CpG316. The
CREB pathway has been implicated in addiction; especially in the Acb, chronic exposure to various drugs of
abuse leads to activation of CREB56, while upon chronic alcohol use, CREB has been suggested to modulate con-
nectivity and synaptic p lasticity9. Herein, the effect of prolonged episodic ethanol consumption was not assessed
on CREB itself, but its potential binding to Vglut3 CpG316 could be blocked by higher methylation in MS360E,
contributing to lower accumbal Vglut3 expression in that group.
Overall, causality could not be assessed in the present study, thus it remains to be investigated whether the
abovementioned signatures precede or follow ELS- and/or ethanol- mediated effects on gene expression. Moreo-
ver, the potential confounding effect of single housing, which can be an extra social stressor in voluntary ethanol
drinking paradigms, on DNA methylation patterns cannot be ruled out. Other epigenetic markers (e.g. histone
modifications) that have been associated with ELS57 and/or ethanol-related phenotypes9 could have been impli-
cated in the previously observed Vgluts expression differences, but they were not assessed herein. Nonetheless,
DNA methylation is considered one of the principal interfaces between the genome and the e nvironment48. Lastly,
the present findings should be interpreted with caution considering that bisulfite-treated DNA was analyzed,
which although widely used, cannot distinguish between 5-methyl- and 5-hydroxymethylcytosine58, limiting
our understanding of each marker’s contribution and potential regulatory effect.
To our knowledge, this is the first study to assess the effect of ELS and subsequent voluntary adult ethanol
drinking on Vglut1-3 DNA methylation patterns in the mesocorticolimbic system of outbred adult male rats, and
calls for further investigation of the reported effects. Future functional studies on Vgluts as well as on downstream
effects of ELS and ethanol on VGLUTs protein expression are guaranteed. Determining epigenetic signatures of
key-neuronal markers such as VGLUTs, the best markers for the glutamatergic phenotype, in key reward and
stress-related brain regions as well as in different stages of ethanol consumption can shed light on the biological
underpinnings of alcohol-related phenotypes and AUD.
Received: 26 February 2021; Accepted: 14 July 2021
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Acknowledgements
The authors want to thank Linnea Granholm for performing the animal experiment, as well as Nicholas Culhane,
Sarah Miller, and Austin Hopkins for performing the assay development, data analysis, and drafting the figures
related to sequencing.
Author contributions
M.V. wrote the first draft of the manuscript and analysed the data; L.Y. and M.V. planned and performed the
genetic analyses; I.N. planned the animal experiment; E.C., I.N., K.W.N. planned the study and secured funds;
E.C. supervised the analyses and drafting of the manuscript; I.N., K.W.N. and Å.W-M. critically revised the
manuscript; All authors reviewed the final draft.
Funding
Open access funding provided by Uppsala University. The funding body had no role in the design of the study,
collection and analysis of data and decision to publish. The present work was partially supported by funds from
the Alcohol Research Council of the Swedish Alcohol Retailing Monopoly, the European Foundation for Alco-
hol Research (EA 11 30), the Swedish Research Council (K2012-61X-22090-01-3) to I.N; the Alcohol Research
Council of the Swedish Alcohol Retailing Monopoly to E.C.; and Swedish Research Council (2013-4657, 2014-
3804), Swedish Brain Foundation, and Research Foundations of Bertil Hållsten and Åhlén to ÅWM. E.C. received
funds from SciLifeLab.
Competing interests
LY is key shareholder of EpigenDx; all genetic analyses were performed in a blind manner. The other authors
have no conflict of interest to declare.
Additional information
Supplementary Information The online version contains supplementary material available at https://doi.org/
10.1038/s41598-021-94739-8.
Correspondence and requests for materials should be addressed to E.C.
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Scientific Reports | (2021) 11:15322 | https://doi.org/10.1038/s41598-021-94739-8 13
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